DB-BERT: a Database Tuning Tool that "Reads the Manual"

TL;DR

DB-BERT is an innovative database tuning tool that uses natural language processing with pre-trained language models to automatically interpret manuals and optimize database parameters through reinforcement learning.

Contribution

It introduces a novel approach combining large language models and reinforcement learning to automate database tuning based on textual hints and system performance.

Findings

DB-BERT outperforms baseline tuning methods in experiments.

It effectively interprets manual instructions for parameter tuning.

Achieves optimal settings across different benchmarks and database systems.

Abstract

DB-BERT is a database tuning tool that exploits information gained via natural language analysis of manuals and other relevant text documents. It uses text to identify database system parameters to tune as well as recommended parameter values. DB-BERT applies large, pre-trained language models (specifically, the BERT model) for text analysis. During an initial training phase, it fine-tunes model weights in order to translate natural language hints into recommended settings. At run time, DB-BERT learns to aggregate, adapt, and prioritize hints to achieve optimal performance for a specific database system and benchmark. Both phases are iterative and use reinforcement learning to guide the selection of tuning settings to evaluate (penalizing settings that the database system rejects while rewarding settings that improve performance). In our experiments, we leverage hundreds of text documents about database tuning as input for DB-BERT. We compare DB-BERT against various baselines, considering different benchmarks (TPC-C and TPC-H), metrics (throughput and run time), as well as database systems (Postgres and MySQL). In all cases, DB-BERT finds the best parameter settings among all compared methods. The code of DB-BERT is available online at https://itrummer.github.io/dbbert/.